Concrete crack segmentation based on convolution–deconvolution feature fusion with holistically nested networks

Xu, Shengjun; Hao, Ming; Liu, Guanghui; Meng, Yuebo; Han, Jiuqiang; Shi, Ya Kun

doi:10.1002/stc.2965

Cited by 16 publications

(4 citation statements)

References 52 publications

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“…The rough set method was the core method of this study. Currently, there are many traditional edge detection operators, such as Canny [32][33][34], Laplacian [35,36], Prewitt [37,38], and Holistically-Nested(HED) [39][40][41]. Many research papers have shown that traditional operators also have better edge detection effects.…”

Section: Edge Detection Modulementioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

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Section: Edge Detection Modulementioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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“…32 In crack detection, there have been attempts to combine the attention mechanism with available networks to improve detection efficiency. [33][34][35] For example, Pan et al 36 modified the backbone of DANet from ResNet101 to VGG19, namely SCHNet, and added a new attention mechanism named feature pyramid attention to improve crack detection accuracy. The results demonstrated that three attention mechanisms can increase MIoU by 10.88% than the baseline model.…”

Section: Introductionmentioning

confidence: 99%

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

Hang

et al. 2023

Structural Health Monitoring

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In this research, an attention-based feature fusion network (AFFNet), with a backbone residual network (ResNet101) enhanced with two attention mechanism modules, is proposed for automatic pixel-level detection of concrete crack. In particular, the inclusion of attention mechanism modules, for example, the vertical and horizontal compression attention module (VH-CAM) and the efficient channel attention upsample module (ECAUM), is to enable selective concentration on the crack feature. The VH-CAM generates a feature map integrating pixel-level information in vertical and horizontal directions. The ECAUM applied on each decoder layer combines efficient channel attention (ECA) and feature fusion, which can provide rich contextual information as guidance to help low-level features recover crack localization. The proposed model is evaluated on the test dataset and the results reach 84.49% for mean intersection over union (MIoU). Comparison with other state-of-the-art models proves high efficiency and accuracy of the proposed method.

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“…Nguyen et al 16 proposed a method for the classification of Alkali‐Silica Reaction (ASR) cracks in bridges using texture analysis and pretrained models using InceptionV3, ResNet‐18, and AlexNet. To address the challenge of crack segmentation in concrete bridges, Xu et al 17 proposed a supervised technique based on holistically nested networks and an encoder‐decoder model that applies VGG‐16 to extract features.…”

Section: Introductionmentioning

confidence: 99%

Crack segmentation in the wild using convolutional neural networks and bootstrapping

Ahmad,

Gharehbaghi,

et al. 2023

Earthquake Engineering and Resilience

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Computer vision and deep learning methods have found numerous practical applications over the last decade. The field of structural health monitoring has greatly benefited from such advancements. Accurate crack detection and segmentation are a critical part of structural health monitoring and assessment. In the past decade, researchers have developed different computer vision and deep learning methods to address this challenging task. In this article, we propose to use convolutional neural networks and bootstrapping to improve crack segmentation in the wild. Specifically, we investigate fully convolutional network (FCN) and DeepLabV3 with different ResNet architectures as the back bone and assess their performance. A unique feature of this work is the use of bootstrapping with a segmentation network and image augmentation. Bootstrapping is an important component of our proposed methodology for better extracting features from the datasets. We also incorporate superpixel pooling for FCN which improves performance. We assess the performance of our method using five publicly available datasets, which include a wide variety of crack images such as thin pavement cracks, tree shadows on cracks, cracks in asphalt roads, concrete wall cracks, wide cracks on concrete surfaces, and so on. A comparative study is also performed with several established methods in the literature. Results indicate that overall, our proposed method outperforms other state‐of‐the‐art methods for crack segmentation.

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Concrete crack segmentation based on convolution–deconvolution feature fusion with holistically nested networks

Cited by 16 publications

References 52 publications

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

Crack segmentation in the wild using convolutional neural networks and bootstrapping

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